{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-28T11:42:39.659656Z",
     "start_time": "2025-03-28T11:42:38.131816Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from binance.spot import Spot\n",
    "from datetime import datetime\n",
    "import pytz\n",
    "import mplfinance as mpf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mplfinance.original_flavor import candlestick_ohlc\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from binance.um_futures import UMFutures\n",
    "\n",
    "# 3. 时间戳转换（含时区处理）\n",
    "def convert_to_beijing_time(ts):\n",
    "    tz = pytz.timezone(\"Asia/Shanghai\")\n",
    "    return datetime.fromtimestamp(ts // 1e3, tz)\n",
    "\n",
    "\n",
    "symbol = \"BTCUSDT\"\n",
    "# symbol = \"BNBUSDT\"\n",
    "# symbol = \"ETHUSDT\"\n",
    "\n",
    "\n",
    "def get_futures_continous_data(symbol=\"BTCUSDT\", interval=\"15m\", limit=\"4\"):\n",
    "    um_futures_client = UMFutures()\n",
    "    klines = um_futures_client.klines(symbol, interval, limit=limit)\n",
    "\n",
    "    # 2. 转换为结构化数据\n",
    "    df = pd.DataFrame(\n",
    "        klines[:-1],  # 不含当前未闭合k线\n",
    "        columns=[\n",
    "            \"Open Time\",\n",
    "            \"open\",\n",
    "            \"high\",\n",
    "            \"low\",\n",
    "            \"close\",\n",
    "            \"Volume\",\n",
    "            \"Close Time\",\n",
    "            \"Quote Asset Volume\",\n",
    "            \"Number of Trades\",\n",
    "            \"Taker Buy Base\",\n",
    "            \"Taker Buy Quote\",\n",
    "            \"Ignore\",\n",
    "        ],\n",
    "    )\n",
    "\n",
    "    df[\"Time\"] = df[\"Open Time\"].apply(convert_to_beijing_time)\n",
    "\n",
    "    # # 4. 构建OHLC数据框架\n",
    "    ohlc = df.copy()\n",
    "    ohlc = ohlc.set_index(\"Time\").astype(float)  # 确保数值类型正确\n",
    "\n",
    "    return ohlc\n",
    "\n",
    "\n",
    "\n",
    "def get_data(interval, limit):\n",
    "    # 1. 获取K线原始数据\n",
    "    client = Spot(base_url=\"https://data-api.binance.vision\")\n",
    "    # interval 1s, 1m, 5m, 1h, 1d,\n",
    "    # 1125-1216 1732475182000-1734289582000 震荡\n",
    "    # 1106-1125 1730833582000-1732475182000 上涨\n",
    "    # 0124 1737659180000\n",
    "    klines = client.klines(\n",
    "        symbol=symbol,\n",
    "        interval=interval,\n",
    "        limit=limit,\n",
    "        # startTime=\"1730833582000\",\n",
    "        # startTime=\"1737659180000\",\n",
    "        # startTime=\"1730833582000\",\n",
    "        # startTime=\"1732475182000\",\n",
    "        # endTime=\"1734289582000\",\n",
    "    )  # 示例：BTC/USDT 1小时K线\n",
    "\n",
    "    # 2. 转换为结构化数据\n",
    "    df = pd.DataFrame(\n",
    "        klines,\n",
    "        columns=[\n",
    "            \"Open Time\",\n",
    "            \"open\",\n",
    "            \"high\",\n",
    "            \"low\",\n",
    "            \"close\",\n",
    "            \"Volume\",\n",
    "            \"Close Time\",\n",
    "            \"Quote Asset Volume\",\n",
    "            \"Number of Trades\",\n",
    "            \"Taker Buy Base\",\n",
    "            \"Taker Buy Quote\",\n",
    "            \"Ignore\",\n",
    "        ],\n",
    "    )\n",
    "\n",
    "    df[\"Time\"] = df[\"Open Time\"].apply(convert_to_beijing_time)\n",
    "\n",
    "    # 4. 构建OHLC数据框架\n",
    "    ohlc = df.copy()\n",
    "    ohlc = ohlc.set_index(\"Time\").astype(float)  # 确保数值类型正确\n",
    "\n",
    "    return ohlc\n",
    "\n",
    "\n",
    "df = get_data(interval=\"15m\", limit=1000)\n",
    "# df = get_futures_continous_data(interval=\"5m\", limit=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-28T11:42:40.958369Z",
     "start_time": "2025-03-28T11:42:40.773489Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class FractalOrderBlockStrategy:\n",
    "    def __init__(self, data, fractal_period=5, fvg_distance=3):\n",
    "        self.data = data  # OHLC数据\n",
    "        self.fractal_period = fractal_period  # 分形检测周期 5\n",
    "        self.fvg_distance = fvg_distance  # FVG最大间隔 3\n",
    "        self.data['mss']=None\n",
    "\n",
    "    def detect_fractals(self):\n",
    "        \"\"\"分形检测逻辑\"\"\"\n",
    "        # 通过滚动窗口检测高低分形\n",
    "        self.data['fractal_highs'] = self.data['high'].rolling(self.fractal_period,  center=True).apply(\n",
    "            lambda x: x.iloc[2] if x.iloc[2] > max(x.iloc[:2]) and x.iloc[2] > max(x.iloc[3:]) else np.nan)\n",
    "        self.data['fractal_lows'] = self.data['low'].rolling(self.fractal_period,  center=True).apply(\n",
    "            lambda x: x.iloc[2] if x.iloc[2] < min(x.iloc[:2]) and x.iloc[2] < min(x.iloc[3:]) else np.nan)\n",
    "        return self.data\n",
    "\n",
    "    def identify_fvg(self):\n",
    "        \"\"\"FVG缺口检测\"\"\"\n",
    "        # 检测bullishImb/bearishImb条件\n",
    "        self.data['bull_fvg'] = (self.data['close'].shift(1) > self.data['high'].shift(2)) & (self.data['low'] > self.data['high'].shift(2))\n",
    "        self.data['bear_fvg'] = (self.data['close'].shift(1) < self.data['low'].shift(2)) & (self.data['high'] < self.data['low'].shift(2))\n",
    "        return self.data\n",
    "\n",
    "    def find_orderblocks_old(self):\n",
    "        \"\"\"订单块生成逻辑\"\"\"\n",
    "        # 结合分形突破与FVG过滤\n",
    "        self.data['ob_bull'] = np.where(\n",
    "            (self.data['close'] > self.data['fractal_highs'].ffill()) &\n",
    "            (self.data['bull_fvg'].rolling(self.fvg_distance).max()), \n",
    "            True, False)\n",
    "\n",
    "        self.data['ob_bear'] = np.where(\n",
    "            (self.data['close'] < self.data['fractal_lows'].ffill()) & \n",
    "            (self.data['bear_fvg'].rolling(self.fvg_distance).max()),  \n",
    "            True, False)\n",
    "        \n",
    "        self.data['ob'] = np.where(\n",
    "            (self.data['close'] > self.data['fractal_highs'].ffill()) & #1,\n",
    "            (self.data['bull_fvg'].rolling(self.fvg_distance).max()), \n",
    "            1, np.where(\n",
    "            (self.data['close'] < self.data['fractal_lows'].ffill()) & #1,\n",
    "            (self.data['bear_fvg'].rolling(self.fvg_distance).max()),  \n",
    "            -1, np.nan))\n",
    "        self.data['ob'] = self.data['ob'].ffill()\n",
    "        \n",
    "        return self.data\n",
    "    \n",
    "    def find_orderblocks(self):\n",
    "        \"\"\"\n",
    "        订单块（Order Block）生成逻辑\n",
    "        \n",
    "        处理步骤：\n",
    "        1. 处理分形高点和低点，获取有效的参考价格\n",
    "        2. 计算公平价值缺口（FVG）的滚动最大值\n",
    "        3. 识别看多和看空订单块\n",
    "        4. 生成综合订单块信号\n",
    "        \n",
    "        Returns:\n",
    "            pd.DataFrame: 包含订单块信号的数据框\n",
    "        \"\"\"\n",
    "        # 1. 处理分形数据，获取有效的参考价格\n",
    "        # - 对fractal_highs使用ffill()填充NaN，然后shift(1)获取上一个高点\n",
    "        # - 这确保我们比较的是上一个有效的分形高点，而不是当前高点\n",
    "        last_valid_fractal_highs = self.data['fractal_highs'].ffill().shift(1)\n",
    "        # - 对fractal_lows只需填充NaN即可，用于当前价格比较\n",
    "        last_valid_fractal_lows = self.data['fractal_lows'].ffill()\n",
    "\n",
    "        # 2. 计算公平价值缺口（FVG）的滚动窗口最大值\n",
    "        # - 使用self.fvg_distance作为窗口大小\n",
    "        # - rolling().max()计算窗口内是否出现过FVG\n",
    "        bull_fvg_max = self.data['bull_fvg'].rolling(self.fvg_distance).max()\n",
    "        bear_fvg_max = self.data['bear_fvg'].rolling(self.fvg_distance).max()\n",
    "\n",
    "        # 3. 识别看多和看空订单块\n",
    "        # 看多订单块条件：\n",
    "        # - 收盘价大于上一个分形高点\n",
    "        # - 且在指定窗口内出现过看多FVG\n",
    "        self.data['ob_bull'] = (self.data['close'] > last_valid_fractal_highs) & bull_fvg_max\n",
    "        \n",
    "        # 看空订单块条件：\n",
    "        # - 收盘价小于当前分形低点\n",
    "        # - 且在指定窗口内出现过看空FVG\n",
    "        self.data['ob_bear'] = (self.data['close'] < last_valid_fractal_lows) & bear_fvg_max\n",
    "\n",
    "        # 4. 生成综合订单块信号\n",
    "        # 使用np.select进行向量化选择：\n",
    "        # - 满足看多条件时返回1\n",
    "        # - 满足看空条件时返回-1\n",
    "        # - 其他情况返回NaN\n",
    "        self.data['ob'] = pd.Series(np.select(\n",
    "            [self.data['ob_bull'], self.data['ob_bear']],  # 条件列表\n",
    "            [1, -1],                                       # 对应的返回值\n",
    "            default=np.nan                                 # 默认值\n",
    "        ), index=self.data.index)\n",
    "\n",
    "        # 5. 使用前向填充处理NaN值\n",
    "        # - 保持最后一个有效信号直到出现新的信号\n",
    "        self.data['ob'] = self.data['ob'].ffill()\n",
    "\n",
    "        self.data['mss'] = self.data['ob'].diff()\n",
    "        self.data['mss'] = self.data['mss'].fillna(value=0)\n",
    "        \n",
    "        return self.data\n",
    "\n",
    "    def plot_signals(self):\n",
    "        \"\"\"可视化信号\"\"\"\n",
    "        plt.figure(figsize=(16,  8))\n",
    "        plt.plot(self.data['close'],  label='Price')\n",
    "        plt.scatter(self.data.index,  self.data['fractal_highs'],  marker=2, c='g', label='Fractal High')\n",
    "        plt.scatter(self.data.index,  self.data['fractal_lows'],  marker=3, c='r', label='Fractal Low')\n",
    "\n",
    "        # 绘制多头订单块信号\n",
    "        # bullish_ob = self.data[self.data['ob_bull']]\n",
    "        # plt.scatter(bullish_ob.index,  bullish_ob['close'], marker='^', c='orange', label='Bullish Order Block')\n",
    "        # \n",
    "        # # 绘制空头订单块信号\n",
    "        # bearish_ob = self.data[self.data['ob_bear']]\n",
    "        # plt.scatter(bearish_ob.index,  bearish_ob['close'], marker='v', c='lime', label='Bearish Order Block')\n",
    "\n",
    "        bull_fvg = self.data[self.data['bull_fvg']]\n",
    "        plt.scatter(bull_fvg.index,  bull_fvg['close'], marker=1, c='orangered', label='Bullish FVG')\n",
    "\n",
    "        bear_fvg = self.data[self.data['bear_fvg']]\n",
    "        plt.scatter(bear_fvg.index,  bear_fvg['close'], marker=0, c='limegreen', label='Bearish FVG')\n",
    "        \n",
    "        bull_mss = self.data[self.data['mss']>0]\n",
    "        plt.scatter(bull_mss.index,  bull_mss['close'], marker=\"o\", c='orangered', label='Bullish MSS')\n",
    "\n",
    "        bear_mss = self.data[self.data['mss'] <0]\n",
    "        plt.scatter(bear_mss.index,  bear_mss['close'], marker=\"o\", c='limegreen', label='Bearish MSS')\n",
    "\n",
    "        plt.legend()\n",
    "        plt.show()\n",
    "\n",
    "    def plot_with_indicators(self):\n",
    "        # 可视化配置\n",
    "        # --------------------------------\n",
    "        # 1. 分形标记\n",
    "        ap1 = mpf.make_addplot(self.data['fractal_highs'],  type='scatter', markersize=80,\n",
    "                               marker='v', color='firebrick', panel=0)\n",
    "        ap2 = mpf.make_addplot(self.data['fractal_lows'],  type='scatter', markersize=80,\n",
    "                               marker='^', color='forestgreen', panel=0)\n",
    "\n",
    "        # 2. 订单块标记\n",
    "        ob_bull = self.data['ob_bull'].replace(False,  np.nan) * self.data['high'] * 1.01\n",
    "        ob_bear = self.data['ob_bear'].replace(False,  np.nan) * self.data['low'] * 0.99\n",
    "        ap3 = mpf.make_addplot(ob_bull,  type='scatter', markersize=120,\n",
    "                               marker='$\\u2191$', color='royalblue', panel=0)\n",
    "        ap4 = mpf.make_addplot(ob_bear,  type='scatter', markersize=120,\n",
    "                               marker='$\\u2193$', color='tomato', panel=0)\n",
    "\n",
    "        # 3. 主图绘制\n",
    "        fig, axes = mpf.plot(self.data,  type='candle', style='yahoo',\n",
    "                             addplot=[ap1, ap2, ap3, ap4],\n",
    "                             figsize=(16, 8), returnfig=True)\n",
    "\n",
    "        # 4. FVG区域绘制（通过Matplotlib直接操作axes）\n",
    "        ax = axes[0]\n",
    "        for i in range(2, len(self.data)):\n",
    "            # 牛市FVG区域（绿色半透明）\n",
    "            if self.data['bull_fvg'].iloc[i]:\n",
    "                x_start = self.data.index[i-2]\n",
    "                x_end = self.data.index[i]\n",
    "                y_top = self.data['high'].iloc[i-2]\n",
    "                y_bottom = self.data['low'].iloc[i]\n",
    "                ax.fill_betweenx([y_top,  y_bottom], x_start, x_end,\n",
    "                                 color='limegreen', alpha=1)\n",
    "\n",
    "            # 熊市FVG区域（红色半透明）\n",
    "            if self.data['bear_fvg'].iloc[i]:\n",
    "                x_start = self.data.index[i-2]\n",
    "                x_end = self.data.index[i]\n",
    "                y_bottom = self.data['low'].iloc[i-2]\n",
    "                y_top = self.data['high'].iloc[i]\n",
    "                ax.fill_betweenx([y_bottom,  y_top], x_start, x_end,\n",
    "                                 color='orangered', alpha=1)\n",
    "\n",
    "        plt.title('Fractal  Orderblocks with FVG Visualization')\n",
    "        plt.show()\n",
    "\n",
    "\n",
    "f = FractalOrderBlockStrategy(data=df)\n",
    "f.detect_fractals()\n",
    "f.identify_fvg()\n",
    "f.find_orderblocks()\n",
    "f.plot_signals()\n",
    "# f.plot_with_indicators()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "import logging\n",
    "from binance.um_futures import UMFutures\n",
    "from binance.lib.utils import config_logging\n",
    "\n",
    "# base_url = \"https://fapi.binance.com\"\n",
    "# key = \"FC9r3f44u9UgPqnG5wzmW1FlTdUrzu8Bu1G3R18kyv1HdtNo7FedLgsuS3oo6bLH\"\n",
    "# secret = \"fjRHyLILCVw0uj29mFTribS94xeFw35SJXjYpkoCsEybPSFeOdrqyBgdzKZkGSBc\"\n",
    "\n",
    "config_logging(logging, logging.DEBUG)\n",
    "um_futures_client = UMFutures()  # key=key,secret=secret,base_url=base_url)\n",
    "\n",
    "logging.info(um_futures_client.klines(\"BTCUSDT\", \"1d\", limit=100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HoangthaiCrypto1994\n",
    "import requests\n",
    "import json\n",
    "\n",
    "url = \"https://www.binance.com/bapi/futures/v1/friendly/future/copy-trade/lead-portfolio/position-history\"\n",
    "\n",
    "payload = json.dumps({\n",
    "    \"pageNumber\": 1,\n",
    "    \"pageSize\": 10,\n",
    "    \"portfolioId\": \"4284698970319737345\",\n",
    "    \"sort\": \"OPENING\"\n",
    "})\n",
    "headers = {\n",
    "    'accept': '*/*',\n",
    "    'accept-language': 'zh-CN,zh;q=0.9,en-US;q=0.8,en;q=0.7',\n",
    "    'bnc-level': '0',\n",
    "    'bnc-location': 'CN',\n",
    "    'bnc-time-zone': 'Asia/Shanghai',\n",
    "    'bnc-uuid': '36f643ec-51c4-4461-8b3c-e057aa78af7b',\n",
    "    'clienttype': 'web',\n",
    "    'content-type': 'application/json',\n",
    "    'csrftoken': 'd41d8cd98f00b204e9800998ecf8427e',\n",
    "    'device-info': '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',\n",
    "    'dnt': '1',\n",
    "    'fvideo-id': '33f68509f3f0af94b471ffc03ac67b244820ca84',\n",
    "    'fvideo-token': 'j81mYDRTKM5pOSGMOoSFy1qYnHP4HnykMQa2QB8LTaHPFqNPJkZUJ4RPpdvmpqtxDSuGrFpupZ7FrVIi/k3VMRyhf18fKw0otb0H3L400c2HhOsfZ8JWkjTStI+gQ9GxptoC1bzGtYsEFCK+BHnMy09hjLgcN/SG1QGAM/kt0RoT/jqQ23MESFDZ1PV1BXC6I=26',\n",
    "    'lang': 'zh-CN',\n",
    "    'origin': 'https://www.binance.com',\n",
    "    'priority': 'u=1, i',\n",
    "    'referer': 'https://www.binance.com/zh-CN/copy-trading/lead-details/4284698970319737345?timeRange=7D',\n",
    "    'sec-ch-ua': '\"Google Chrome\";v=\"135\", \"Not-A.Brand\";v=\"8\", \"Chromium\";v=\"135\"',\n",
    "    'sec-ch-ua-mobile': '?0',\n",
    "    'sec-ch-ua-platform': '\"macOS\"',\n",
    "    'sec-fetch-dest': 'empty',\n",
    "    'sec-fetch-mode': 'cors',\n",
    "    'sec-fetch-site': 'same-origin',\n",
    "    'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/135.0.0.0 Safari/537.36',\n",
    "    'x-passthrough-token': '',\n",
    "    'x-trace-id': '789dcc89-897e-48a4-a8da-6472e890f75b',\n",
    "    'x-ui-request-trace': '789dcc89-897e-48a4-a8da-6472e890f75b'\n",
    "}\n",
    "\n",
    "response = requests.request(\"POST\", url, headers=headers, data=payload)\n",
    "\n",
    "print(response.text)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
